Package arulesnbminer

Transcription

1 Version Date Package arulesnbminer July 2, 2015 Title Mining NB-Frequent Itemsets and NB-Precise Rules Author Michael Hahsler Maintainer Michael Hahsler Description NBMiner is an implementation of the model-based mining algorithm for mining NB-frequent itemsets presented in ``Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2): , September 2006.'' In addition an extension for NB-precise rules is implemented. Depends R (>= 2.10), arules (>= 0.6-6), rjava (>= 0.6-3) URL Imports methods, stats, graphics SystemRequirements Java (>= 5.0) License GPL-2 NeedsCompilation no Repository CRAN Date/Publication :42:39 R topics documented: Agrawal NBMiner NBMinerParameters Index 6 1

2 2 Agrawal Agrawal Synthetic Example Dataset Agrawal Description This dataset is generated by the method described by Agrawal and Srikant (1994) using the reimplementation in arules which also retains the patterns used in the generation process. Usage data(agrawal) Format The format is: transactions Agrawal.db itemsets Agrawal.pat Details Agrawal.db contains the dataset (1000 items/20000 transactions) and Agrawal.pat contains the patterns that were used to create the dataset. References Rakesh Agrawal and Ramakrishnan Srikant (1994). Fast algorithms for mining association rules in large databases. In Jorge B. Bocca, Matthias Jarke, and Carlo Zaniolo, editors, Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, pages , Santiago, Chile. Examples data(agrawal) summary(agrawal.pat) summary(agrawal.db) ## the data sets was generated with the following code ## Not run: Agrawal.pat <- random.patterns(1000, npats = 2000, method = "agrawal", lpats = 2, corr = 0.5, cmean = 0.5, cvar = 0.1, iweight = NULL, verbose = FALSE) Agrawal.db <- random.transactions(1000, 20000, method="agrawal", patterns = Agrawal.pat) ## End(Not run)

3 NBMiner 3 NBMiner NBMiner: Mine NB-Frequent Itemsets or NB-Precise Rules Description Usage Calls the Java implementation of the depth first search algorithm described in the paper in the references section to mine NB-frequent itemsets of NB-precise rules. NBMiner(data, parameter, control = NULL) Arguments data parameter control object of class transactions. a list of parameters (automatically converted into an object of class NBMinerParameter). Reasonable parameters can be obtained using NBMinerParameters (see details section). a list of control options (automatically converted into an object of class NBMinerControl). Currently only "verbose" and "debug" (both logical) are available. Details Value The parameters can be estimated from the data using NBMinerParameters. An object of class itemsets or rules (depending on the rules entry in parameter). The estimated precision is stored in the quality slot. References Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery, 13(2): , September See Also NBMinerParameters, transactions-class, itemsets-class, rules-class Examples data("agrawal") ## mine param <- NBMinerParameters(Agrawal.db, pi=0.99, theta=0.5, maxlen=5, minlen=1, trim = 0, verb = TRUE, plot=true) itemsets_nb <- NBMiner(Agrawal.db, parameter = param, control = list(verb = TRUE, debug=false))

4 4 NBMinerParameters inspect(head(itemsets_nb)) ## remove patterns of length 1 (noise) i_nb <- itemsets_nb[size(itemsets_nb)>1] patterns <- Agrawal.pat[size(Agrawal.pat)>1] ## how many found itemsets are subsets of the patterns used in the db? table(rowsums(is.subset(i_nb,patterns))>0) ## compare with the same number of the most frequent itemsets itemsets_supp <- eclat(agrawal.db, parameter=list(supp=0.001)) i_supp <- itemsets_supp[size(itemsets_supp) >1] i_supp <- head(sort(i_supp, by = "support"), length(i_nb)) table(rowsums(is.subset(i_supp,patterns))>0) ## mine NB-precise rules param <- NBMinerParameters(Agrawal.db, pi=0.99, theta=0.5, maxlen=5, rules=true, minlen=1, trim = 0) rules_nb <- NBMiner(Agrawal.db, parameter = param, control = list(verb = TRUE, debug=false)) inspect(head(rules_nb)) NBMinerParameters Estimate Global Model Parameters from Data Description Usage Estimate the global negative binomial data model used by the NBMiner and create an appropriate parameter object. NBMinerParameters(data, trim = 0.01, pi = 0.99, theta = 0.5, minlen = 1, maxlen = 5, rules = FALSE, plot = FALSE, verbose = FALSE, getdata = FALSE) Arguments data trim the data as a object of class transactions. fraction of incidences to trim off the tail of the frequency distribution of the data. pi precision threshold π. theta pruning parameter θ. minlen minimum number of items in found itemsets (default: 1). maxlen maximal number of items in found itemsets (default: 5). rules mine NB-precise rules instead of NB-frequent itemsets?

5 NBMinerParameters 5 plot verbose getdata plot the model? use verbose output for the estimation procedure. get also the observed and estimated counts. Details Value Uses the EM algorithm to estimate the global NB model for the data. The EM algorithm is used since the zero class (items which do not occur in the dataset) is not included in the data. The result are the two NB parameters k and a, where a is rescaled by dividing it by the number of incidences in the data (this is needed by the NBMiner). Also the real number of items n is a result of the estimation. theta and pi are just taken and added to the resulting parameter object. an object of class NBMinerParameter for NBMiner. References Michael Hahsler. A model-based frequency constraint for mining associations from transaction data. Data Mining and Knowledge Discovery,13(2): , September See Also NBMiner, transactions-class Examples data("epub") param <- NBMinerParameters(Epub, trim = 0.05, plot = TRUE, verbose = TRUE) param

6 Index Topic datasets Agrawal, 2 Topic models NBMiner, 3 NBMinerParameters, 4 Agrawal, 2 NBMiner, 3, 5 NBMinerControl-class (NBMiner), 3 NBMinerParameter-class (NBMiner), 3 NBMinerParameters, 3, 4 6